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Chapter 6
Analyzing Customer
Behavior Using Online
Analytical Mining (OLAM)
Thanachart Ritbumroong
King Mongkut’s University of Technology Thonburi, Thailand
ABSTRACT
Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this
integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to
choose a particular portion of data and analyze them with data mining models. Previous studies have
provided examples of OLAM applications with the motivation to improve technical performance. This
chapter reviews the capabilities of OLAM and discusses the well-known concept encompassing the
analysis of customer behavior. The underlying motivation of this chapter is to present the opportunities
for the development of OLAM to support the customer behavior analysis. Three main directions of the
advancement in OLAM are proposed for future research.
INTRODUCTION
The ability to analyze customer behavior to identify high-value customers and retain them has
become the key success factor for a company (J.
H. Lee & Park, 2005). To achieve this, customer
data must be integrated from various sources and
analyzed to help business learn about customer
behavior and better plan marketing campaigns
to strengthen customer relationship. Business
Intelligence (BI) is one of the key technologies
employed to consolidate and transform customer
data into customer intelligence (Chen & Popovich,
2003). With the On-line Analytical Processing
(OLAP) reporting ability, it provides users with
multidimensional hierarchical data which business
users can drill-down into more detailed data. This
would require users’ skills using reporting features
in data analysis. Relying on a human’s ability
to analyze data could lead to errors in decisionmaking, especially with the case of using OLAP.
Users need to use OLAP capabilities to discovery
patterns hidden in the data by themselves. Peng,
Viator, and Buchheit (2007) found that users
did not put full effort in the data analysis. Users
were found to make suboptimal economic deci-
DOI: 10.4018/978-1-4666-6477-7.ch006
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Analyzing Customer Behavior Using Online Analytical Mining (OLAM)
sions by choosing to investigate only information
at an aggregate level. Consequently, they made
incorrect decisions by ignoring information at
the detailed level.
As data volumes grow rapidly, manual data
analysis seems to be impractical. Data mining is
another key technology utilizing machine learning
algorithms to extract patterns from data (Fayyad,
Piatetsky-Shapiro, & Smyth, 1996a). These algorithms are designed to handle large-scale data
effectively. Customer behavioral patterns can be
extracted from transactions recorded. Business
can learn about customer preferences and predict
next best offerings. Although data mining models
could be used to extract knowledge from databases,
they are too complicated for users to interact with
(Azevedo & Santos, 2011). If data mining can be
simplified as step-by-step interactive data analysis
like the OLAP reporting functionality, the process
of knowledge extraction will be more flexible and
transparent to users. Hence, Han (1998) proposed
the architecture for On-line Analytical Mining
(OLAM) to integrate data mining into OLAP. With
this integration, data mining algorithms can be
performed with OLAP abilities enabling different
portions of data to be analyzed with data mining
models. The flexibility of the analysis allows users
to understand the process of data mining. Users
can choose a portion of data in interest and use
data mining techniques to analyze them during
the process of the analysis.
Previous studies have discussed different architectures of OLAM. Usman and Asghar (2011)
demonstrated the use of hierarchical clustering
to pre-process data. The results of the clustering
were automatically generated as a dimension for
an OLAP cube providing a new insight into data
analysis. Kwan, Fong, and Wong (2005) used
an OLAM methodology to discover customer
behavioral changes on a web site by applying
two measures of association analysis; support
and confidence levels, to identify the association
semantics of customer behavioral sequences on
web pages. The discovered association rules are
constantly updated into the OLAM models to
reflect the actual on-line behaviors. Gao, Ni, and
Zhao (2009) proposed a scheduling strategy for
the OLAM process. OLAM tasks were classified
into query tasks, mining tasks and updating tasks.
The tasks were submitted simultaneously in order
to reduce system workload. Evidently, data mining
techniques can be used in combination with OLAP
technology to gain a higher degree of flexibility
in the data analysis. OLAM is believed to help
users perform data analysis more efficiently and
better interact with results of the analysis.
This chapter aims to summarize previous studies related to OLAM and discuss different OLAM
architectures. Since most of the previous studies
focused on the technical aspect of OLAM, the
main contribution of this proposed chapter is to
provide discussion from a business point of views.
The emphasis will be on the practical benefits
that users will gain from the use of OLAM. The
organization of this chapter will be as follow.
The first section of the chapter will discuss the
general architecture of OLAM and its capabilities.
Next, OLAP and data mining techniques typically
employed for the customer behavior analysis are
discussed. An OLAM architecture that combines
previously discussed OLAP and data mining
techniques capabilities will be proposed. Discussion regarding the practical usage of this OLAM
architecture will be provided and justified. Finally,
the chapter will end with future research directions
and conclusion.
BACKGROUND
Online Analytical Mining (OLAM):
An Architecture Overview
Online Analytical Mining (OLAM) refers to the
integration between Online-Analytical Processing
and Data Mining in order to incorporate more
sophisticated data processing techniques to users (Han, 1998). OLAP has been known to offer
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